Infrared moving point target detection using a spatial-temporal filter

Abstract Infrared point target detection technology has widely applications, such as in tracking and surveillance system. In this paper, an moving point target detection method is proposed to detect targets from infrared sequence images. Firstly, a modified spatial-temporal total variation model is proposed for background prediction. Then the subtraction image is obtained by subtracting the predicted background from the corresponding sequence image. Finally, the target can be detected from the result that the product of the subtraction image and a temporal contrast filter. The effectiveness of the modified spatial-temporal filter on infrared moving point targets detection is verified by detecting targets from different infrared sequence images.

[1]  Huchuan Lu,et al.  Robust Visual Tracking via Least Soft-Threshold Squares , 2016, IEEE Transactions on Circuits and Systems for Video Technology.

[2]  Kyu-Ik Sohng,et al.  An Efficient Two-Dimensional Least Mean Square (TDLMS) Based on Block Statistics for Small Target Detection , 2009 .

[3]  Yansheng Li,et al.  Adaptive top-hat filter based on quantum genetic algorithm for infrared small target detection , 2018, Multimedia Tools and Applications.

[4]  Yantao Wei,et al.  Background suppression of small target image based on fast local reverse entropy operator , 2013, IET Comput. Vis..

[5]  Hong Li,et al.  Small infrared target detection based on harmonic and sparse matrix decomposition , 2013 .

[6]  Zhong Chen,et al.  Infrared small target detection based on modified local entropy and EMD , 2010 .

[7]  Yansheng Li,et al.  Infrared moving point target detection based on an anisotropic spatial-temporal fourth-order diffusion filter , 2018, Comput. Electr. Eng..

[8]  Delian Liu,et al.  Temporal Profile Based Small Moving Target Detection Algorithm in Infrared Image Sequences , 2007 .

[9]  Yantao Wei,et al.  Infrared moving point target detection based on spatial–temporal local contrast filter , 2016 .

[10]  Hu Zhu,et al.  Moving point target detection based on clutter suppression using spatiotemporal local increment coding , 2015 .

[11]  Lei Yang,et al.  Adaptive detection for infrared small target under sea-sky complex background , 2004 .

[12]  Xiangzhi Bai,et al.  Analysis of new top-hat transformation and the application for infrared dim small target detection , 2010, Pattern Recognit..

[13]  G.-D. Wang,et al.  Facet-based infrared small target detection method , 2005 .

[14]  Huchuan Lu,et al.  Inverse Sparse Tracker With a Locally Weighted Distance Metric , 2015, IEEE Transactions on Image Processing.

[15]  Raveendran Paramesran,et al.  Image denoising using combined higher order non-convex total variation with overlapping group sparsity , 2019, Multidimens. Syst. Signal Process..

[16]  Wei Zheng,et al.  Moving Point Target Detection Based on Higher Order Statistics in Very Low SNR , 2018, IEEE Geoscience and Remote Sensing Letters.

[17]  Xiangzhi Bai,et al.  Infrared small target enhancement and detection based on modified top-hat transformations , 2010, Comput. Electr. Eng..

[18]  Wei Huang,et al.  A novel evaluation metric based on visual perception for moving target detection algorithm , 2016 .

[19]  Tae-Wuk Bae,et al.  Small target detection using bilateral filter and temporal cross product in infrared images , 2011 .

[20]  Xiangyang Xue,et al.  Patch Image Based LSMR Method for Moving Point Target Detection , 2017, IFIP TC12 ICIS.

[21]  Zhang Peng,et al.  The design of Top-Hat morphological filter and application to infrared target detection , 2006 .

[22]  Xin Tian,et al.  Directional support value of Gaussian transformation for infrared small target detection. , 2015, Applied optics.

[23]  Meng Hwa Er,et al.  Max-mean and max-median filters for detection of small targets , 1999, Optics & Photonics.

[24]  Yao Zhao,et al.  Bilateral two-dimensional least mean square filter for infrared small target detection , 2014 .

[25]  David W. Thomas,et al.  The two-dimensional adaptive LMS (TDLMS) algorithm , 1988 .